Product Manager - Auto Suggest & Vendor Onboarding

Product Manager

Search (Autosuggest - Freetext - Voice - AI Enhancement) | AI Chat & Chatbots | Book Appointments & Vendor Onboarding

Full-time - 4-6 Years Experience - Mid-Level - Search, AI , Conversational Product & Book Appointment feature

About the Role

We are looking for a product-minded, AI-fluent Product Manager to work on three high-impact areas. First, our Search platform - spanning autosuggest, freetext, and voice search, with AI enhancements that make suggestions smarter and results more relevant. Second, our AI Chat & Chatbot experiences - including customer-facing chat assistants and automated chatbot flows powered by LLMs. Third, our Appointments & Vendor ecosystem - covering the end-to-end book appointment feature and the vendor onboarding module.

You combine a strong foundation in search product fundamentals with real experience building AI-powered conversational interfaces. You understand how users interact with both search and chat - and how these surfaces increasingly converge. You are equally comfortable in user research, writing detailed PRDs, partnering with ML/NLP teams, and driving cross-functional delivery at pace.

Key Responsibilities

Search - Autosuggest, Freetext, Voice & AI Enhancement

- Own the full search product roadmap - from core autosuggest and freetext relevance to voice query handling and AI-powered result enhancement.

- Drive the autosuggest experience: improve query completion quality, handle partial/misspelled inputs, and personalise suggestions based on user context and history.

- Own freetext search quality - define ranking logic, reduce zero-result rates, handle long-tail queries, and improve precision and recall.

- Lead voice search product: define the end-to-end voice query flow, work with NLP teams on intent recognition, and ensure a seamless voice-to-results experience across platforms.

- Layer AI enhancements on top of core search - including semantic search, embedding-based retrieval, query expansion, and LLM-assisted result re-ranking to improve suggestion quality and search relevance.

- Define and track the full suite of search quality metrics: Query success rate, click-through rate, zero-result rate, autosuggest acceptance rate, voice recognition accuracy, and AI uplift metrics.

- Design and run A/B and multivariate experiments across all search modalities; synthesise results into clear roadmap decisions.

AI Chat Assistants & Chatbots

- Own the product strategy and roadmap for AI-powered chat assistants and chatbot experiences across customer-facing surfaces.

- Define conversation design principles, user flows, and escalation paths - balancing automation with seamless human handoff where needed.

- Work closely with LLM/NLP engineers to define prompt strategies, context management, tone and safety guardrails, and response quality benchmarks.

- Build and track chatbot performance metrics: containment rate, resolution rate, user satisfaction (CSAT), fallback rate, and session completion rate.

- Identify use cases where the chat assistant can intelligently connect to search, appointments, or vendor discovery - creating a unified, conversational product experience.

- Run experiments to improve bot response quality, intent accuracy, and conversation completion - and translate results into prompt, flow, or model improvements.

- Stay current on LLM product developments - including tool-use, RAG-based chat, memory and context management, and agentic chat - and apply relevant patterns to your roadmap.

Book Appointments & Vendor Onboarding

- Has experience in end-to-end product strategy and execution for the book-an-appointment feature - including discovery, scheduling flows, calendar syncing, confirmations, reminders, and cancellation/rescheduling handling.

- Lead the vendor onboarding module from sign-up and profile creation through service listing, availability setup, calendar syncing and go-live - ensuring vendors can get started quickly and independently.

- Define and improve key funnel metrics: appointment conversion rate, vendor activation rate, time-to-first-booking, and drop-off points across both user and vendor journeys.

- Collaborate with sales, operations, vendor success, and support teams to surface friction points and translate them into product improvements.

- Explore opportunities to use the AI chat assistant to streamline appointment booking - enabling users to book via conversation rather than only through traditional UI flows.

What We're Looking For

Required

- 4-6 years of product management experience, with hands-on ownership of search products (autosuggest, freetext, or discovery) and/or AI-powered chat or chatbot products.

- Proven experience shipping and improving conversational AI products - chat assistants, LLM-powered bots, or rule-based chatbot flows - with measurable impact on resolution or engagement metrics.

- Solid understanding of how AI/ML powers search and chat - including semantic search, embeddings, NLP intent recognition, LLM prompt design, RAG, and conversation state management.

- Proven track record of shipping end-to-end features with measurable business impact, including well-designed A/B experiments.

- Strong analytical mindset - comfortable with SQL, funnel analysis, and product analytics tools

- Ability to write clear, structured PRDs covering user flows, conversation flows, edge cases, and technical constraints.

- Experience with voice search or voice-based interfaces is a strong plus.

- Strong communication and stakeholder management skills across technical and non-technical audiences.

Nice to Have

- Experience with agentic AI or tool-use patterns - where the chatbot takes actions (e.g. booking an appointment, searching for vendors) on behalf of the user.

- Familiarity with conversation design best practices, including intent mapping, entity extraction, slot-filling, and fallback handling.

- Prior experience with appointment scheduling, booking platforms, or marketplace vendor onboarding.

- Exposure to responsible AI practices - hallucination mitigation, bias detection, safety guardrails, and user trust in AI-generated responses.

- Experience evaluating or integrating third-party LLM APIs (e.g. OpenAI, Anthropic, Gemini) or chatbot platforms.

What You'll Bring

- Deep curiosity about how people search and converse - and a drive to make both experiences faster, smarter, and more effortless.

- The ability to think in systems: you see how search, chat, and transactional flows (appointments, onboarding) connect, and you design for the whole.

- Comfort bridging the gap between what AI can do and what users actually need - you make AI invisible and outcomes obvious.

- Comfort with ambiguity, especially in probabilistic AI contexts where outputs vary and ground truth is hard to define.

- A collaborative spirit - you treat ML engineers, data scientists, conversation designers, and ops partners as co-owners of outcomes.

- Rigour and speed in equal measure - you move fast, instrument first, and celebrate learnings as much as wins.